Overview

Dataset statistics

Number of variables21
Number of observations2954
Missing cells2565
Missing cells (%)4.1%
Duplicate rows16
Duplicate rows (%)0.5%
Total size in memory484.8 KiB
Average record size in memory168.0 B

Variable types

Numeric20
Categorical1

Alerts

Dataset has 16 (0.5%) duplicate rowsDuplicates
Продолжительность жизни is highly overall correlated with Смертность среди взрослых and 12 other fieldsHigh correlation
Смертность среди взрослых is highly overall correlated with Продолжительность жизни and 2 other fieldsHigh correlation
Смертность младенцев is highly overall correlated with Продолжительность жизни and 5 other fieldsHigh correlation
Алкоголь is highly overall correlated with Индекс состава доходов and 2 other fieldsHigh correlation
Процентное использование расходов is highly overall correlated with ВВП and 1 other fieldsHigh correlation
Гепатит B is highly overall correlated with Полио and 1 other fieldsHigh correlation
Корь is highly overall correlated with Смертность младенцев and 1 other fieldsHigh correlation
ИМТ is highly overall correlated with Продолжительность жизни and 5 other fieldsHigh correlation
Смертность детей до 5 лет is highly overall correlated with Продолжительность жизни and 6 other fieldsHigh correlation
Полио is highly overall correlated with Продолжительность жизни and 4 other fieldsHigh correlation
Дифтерия is highly overall correlated with Продолжительность жизни and 4 other fieldsHigh correlation
ВИЧ/СПИД is highly overall correlated with Продолжительность жизни and 5 other fieldsHigh correlation
ВВП is highly overall correlated with Продолжительность жизни and 5 other fieldsHigh correlation
Худоба 1-19 лет is highly overall correlated with Продолжительность жизни and 4 other fieldsHigh correlation
Худоба 5-9 лет is highly overall correlated with Продолжительность жизни and 4 other fieldsHigh correlation
Индекс состава доходов is highly overall correlated with Продолжительность жизни and 14 other fieldsHigh correlation
Образование is highly overall correlated with Продолжительность жизни and 12 other fieldsHigh correlation
Статус is highly overall correlated with Продолжительность жизни and 3 other fieldsHigh correlation
Алкоголь has 195 (6.6%) missing valuesMissing
Гепатит B has 553 (18.7%) missing valuesMissing
ИМТ has 34 (1.2%) missing valuesMissing
Общие расходы has 227 (7.7%) missing valuesMissing
ВВП has 448 (15.2%) missing valuesMissing
Население has 652 (22.1%) missing valuesMissing
Худоба 1-19 лет has 34 (1.2%) missing valuesMissing
Худоба 5-9 лет has 34 (1.2%) missing valuesMissing
Индекс состава доходов has 167 (5.7%) missing valuesMissing
Образование has 163 (5.5%) missing valuesMissing
Смертность младенцев has 848 (28.7%) zerosZeros
Процентное использование расходов has 617 (20.9%) zerosZeros
Корь has 989 (33.5%) zerosZeros
Смертность детей до 5 лет has 785 (26.6%) zerosZeros
Индекс состава доходов has 130 (4.4%) zerosZeros

Reproduction

Analysis started2023-04-03 12:24:05.144415
Analysis finished2023-04-03 12:24:24.910856
Duration19.77 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Год
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5186
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:24.941738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6138191
Coefficient of variation (CV)0.0022982696
Kurtosis-1.2136987
Mean2007.5186
Median Absolute Deviation (MAD)4
Skewness-0.0063742758
Sum5930210
Variance21.287327
MonotonicityNot monotonic
2023-04-03T15:24:24.980156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2013 194
 
6.6%
2015 184
 
6.2%
2014 184
 
6.2%
2012 184
 
6.2%
2011 184
 
6.2%
2010 184
 
6.2%
2009 184
 
6.2%
2008 184
 
6.2%
2007 184
 
6.2%
2006 184
 
6.2%
Other values (6) 1104
37.4%
ValueCountFrequency (%)
2000 184
6.2%
2001 184
6.2%
2002 184
6.2%
2003 184
6.2%
2004 184
6.2%
2005 184
6.2%
2006 184
6.2%
2007 184
6.2%
2008 184
6.2%
2009 184
6.2%
ValueCountFrequency (%)
2015 184
6.2%
2014 184
6.2%
2013 194
6.6%
2012 184
6.2%
2011 184
6.2%
2010 184
6.2%
2009 184
6.2%
2008 184
6.2%
2007 184
6.2%
2006 184
6.2%

Статус
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.2 KiB
Развивающаяся
2442 
Развитая
512 

Length

Max length13
Median length13
Mean length12.133378
Min length8

Characters and Unicode

Total characters35842
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowРазвивающаяся
2nd rowРазвивающаяся
3rd rowРазвивающаяся
4th rowРазвивающаяся
5th rowРазвивающаяся

Common Values

ValueCountFrequency (%)
Развивающаяся 2442
82.7%
Развитая 512
 
17.3%

Length

2023-04-03T15:24:25.022435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-03T15:24:25.071235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
развивающаяся 2442
82.7%
развитая 512
 
17.3%

Most occurring characters

ValueCountFrequency (%)
а 8350
23.3%
в 5396
15.1%
я 5396
15.1%
Р 2954
 
8.2%
з 2954
 
8.2%
и 2954
 
8.2%
ю 2442
 
6.8%
щ 2442
 
6.8%
с 2442
 
6.8%
т 512
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32888
91.8%
Uppercase Letter 2954
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 8350
25.4%
в 5396
16.4%
я 5396
16.4%
з 2954
 
9.0%
и 2954
 
9.0%
ю 2442
 
7.4%
щ 2442
 
7.4%
с 2442
 
7.4%
т 512
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
Р 2954
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 35842
100.0%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а 8350
23.3%
в 5396
15.1%
я 5396
15.1%
Р 2954
 
8.2%
з 2954
 
8.2%
и 2954
 
8.2%
ю 2442
 
6.8%
щ 2442
 
6.8%
с 2442
 
6.8%
т 512
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 35842
100.0%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
а 8350
23.3%
в 5396
15.1%
я 5396
15.1%
Р 2954
 
8.2%
з 2954
 
8.2%
и 2954
 
8.2%
ю 2442
 
6.8%
щ 2442
 
6.8%
с 2442
 
6.8%
т 512
 
1.4%
Distinct362
Distinct (%)12.3%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean69.123098
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.111571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.2
Q163
median72
Q375.6
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation9.6097458
Coefficient of variation (CV)0.13902366
Kurtosis-0.24707131
Mean69.123098
Median Absolute Deviation (MAD)5.8
Skewness-0.64007331
Sum203498.4
Variance92.347214
MonotonicityNot monotonic
2023-04-03T15:24:25.164304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 45
 
1.5%
75 33
 
1.1%
78 31
 
1.0%
73.6 28
 
0.9%
76 25
 
0.8%
73.9 25
 
0.8%
81 25
 
0.8%
74.5 24
 
0.8%
74.7 24
 
0.8%
74.1 23
 
0.8%
Other values (352) 2661
90.1%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
41.5 1
< 0.1%
42.3 1
< 0.1%
43.1 1
< 0.1%
43.3 1
< 0.1%
43.5 1
< 0.1%
43.8 1
< 0.1%
44 1
< 0.1%
ValueCountFrequency (%)
89 11
0.4%
88 10
0.3%
87 9
0.3%
86 15
0.5%
85 12
0.4%
84 11
0.4%
83.7 1
 
< 0.1%
83.5 2
 
0.1%
83.4 1
 
< 0.1%
83.3 1
 
< 0.1%
Distinct425
Distinct (%)14.4%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean166.41372
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.215563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3229
95-th percentile413
Maximum723
Range722
Interquartile range (IQR)155

Descriptive statistics

Standard deviation127.05377
Coefficient of variation (CV)0.7634813
Kurtosis1.9593355
Mean166.41372
Median Absolute Deviation (MAD)76
Skewness1.23207
Sum489922
Variance16142.659
MonotonicityNot monotonic
2023-04-03T15:24:25.264601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 34
 
1.2%
14 30
 
1.0%
16 29
 
1.0%
138 25
 
0.8%
11 25
 
0.8%
19 23
 
0.8%
144 22
 
0.7%
15 21
 
0.7%
17 21
 
0.7%
13 21
 
0.7%
Other values (415) 2693
91.2%
ValueCountFrequency (%)
1 12
0.4%
2 8
 
0.3%
3 6
 
0.2%
4 4
 
0.1%
5 2
 
0.1%
6 13
0.4%
7 17
0.6%
8 13
0.4%
9 12
0.4%
11 25
0.8%
ValueCountFrequency (%)
723 2
0.1%
717 2
0.1%
715 2
0.1%
699 1
< 0.1%
693 1
< 0.1%
686 2
0.1%
682 1
< 0.1%
679 1
< 0.1%
675 1
< 0.1%
666 1
< 0.1%

Смертность младенцев
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.283683
Minimum0
Maximum1800
Zeros848
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.317178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q323
95-th percentile94
Maximum1800
Range1800
Interquartile range (IQR)23

Descriptive statistics

Standard deviation117.60704
Coefficient of variation (CV)3.8835118
Kurtosis116.69492
Mean30.283683
Median Absolute Deviation (MAD)3
Skewness9.8139531
Sum89458
Variance13831.416
MonotonicityNot monotonic
2023-04-03T15:24:25.371000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 848
28.7%
1 342
 
11.6%
2 203
 
6.9%
3 175
 
5.9%
4 96
 
3.2%
8 57
 
1.9%
7 53
 
1.8%
9 48
 
1.6%
10 48
 
1.6%
6 46
 
1.6%
Other values (199) 1038
35.1%
ValueCountFrequency (%)
0 848
28.7%
1 342
11.6%
2 203
 
6.9%
3 175
 
5.9%
4 96
 
3.2%
5 44
 
1.5%
6 46
 
1.6%
7 53
 
1.8%
8 57
 
1.9%
9 48
 
1.6%
ValueCountFrequency (%)
1800 2
0.1%
1700 2
0.1%
1600 1
< 0.1%
1500 2
0.1%
1400 1
< 0.1%
1300 2
0.1%
1200 1
< 0.1%
1100 2
0.1%
1000 1
< 0.1%
957 1
< 0.1%

Алкоголь
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1076
Distinct (%)39.0%
Missing195
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean4.6022037
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.423652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.89
median3.77
Q37.68
95-th percentile11.96
Maximum17.87
Range17.86
Interquartile range (IQR)6.79

Descriptive statistics

Standard deviation4.0427406
Coefficient of variation (CV)0.87843583
Kurtosis-0.79324349
Mean4.6022037
Median Absolute Deviation (MAD)3.24
Skewness0.59088482
Sum12697.48
Variance16.343752
MonotonicityNot monotonic
2023-04-03T15:24:25.473371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 288
 
9.7%
0.03 15
 
0.5%
0.04 13
 
0.4%
0.02 12
 
0.4%
0.09 12
 
0.4%
0.06 10
 
0.3%
1.18 10
 
0.3%
0.21 10
 
0.3%
0.55 9
 
0.3%
0.49 9
 
0.3%
Other values (1066) 2371
80.3%
(Missing) 195
 
6.6%
ValueCountFrequency (%)
0.01 288
9.7%
0.02 12
 
0.4%
0.03 15
 
0.5%
0.04 13
 
0.4%
0.05 9
 
0.3%
0.06 10
 
0.3%
0.07 4
 
0.1%
0.08 9
 
0.3%
0.09 12
 
0.4%
0.1 7
 
0.2%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%
16.58 1
< 0.1%
16.35 1
< 0.1%
15.52 1
< 0.1%
15.19 1
< 0.1%
15.14 1
< 0.1%
15.07 1
< 0.1%
15.04 2
0.1%

Процентное использование расходов
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2328
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean734.36294
Minimum0
Maximum19479.912
Zeros617
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.527553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.4411579
median63.855205
Q3437.07571
95-th percentile4484.387
Maximum19479.912
Range19479.912
Interquartile range (IQR)432.63455

Descriptive statistics

Standard deviation1983.2234
Coefficient of variation (CV)2.7006038
Kurtosis26.728868
Mean734.36294
Median Absolute Deviation (MAD)63.855205
Skewness4.6653694
Sum2169308.1
Variance3933174.9
MonotonicityNot monotonic
2023-04-03T15:24:25.578732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 617
 
20.9%
8.7174087 2
 
0.1%
53.30858081 2
 
0.1%
10.82259524 2
 
0.1%
92.60233636 2
 
0.1%
63.75053034 2
 
0.1%
10.66670728 2
 
0.1%
1.040021112 2
 
0.1%
20.84342867 2
 
0.1%
29.81456607 2
 
0.1%
Other values (2318) 2319
78.5%
ValueCountFrequency (%)
0 617
20.9%
0.09987219 1
 
< 0.1%
0.108055973 1
 
< 0.1%
0.27564826 1
 
< 0.1%
0.328418056 1
 
< 0.1%
0.358651421 1
 
< 0.1%
0.388253772 1
 
< 0.1%
0.397228764 1
 
< 0.1%
0.442802404 1
 
< 0.1%
0.5305728 1
 
< 0.1%
ValueCountFrequency (%)
19479.91161 1
< 0.1%
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%
18822.86732 1
< 0.1%
18379.32974 1
< 0.1%
17028.52798 1
< 0.1%
16255.16198 1
< 0.1%
15515.75234 1
< 0.1%
15345.4907 1
< 0.1%
15268.06445 1
< 0.1%

Гепатит B
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)3.6%
Missing553
Missing (%)18.7%
Infinite0
Infinite (%)0.0%
Mean80.871304
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.633447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q177
median92
Q397
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.088271
Coefficient of variation (CV)0.31022464
Kurtosis2.7537565
Mean80.871304
Median Absolute Deviation (MAD)6
Skewness-1.9260108
Sum194172
Variance629.42135
MonotonicityNot monotonic
2023-04-03T15:24:25.683962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 240
 
8.1%
98 210
 
7.1%
96 167
 
5.7%
97 156
 
5.3%
95 150
 
5.1%
94 128
 
4.3%
93 101
 
3.4%
92 92
 
3.1%
91 76
 
2.6%
89 71
 
2.4%
Other values (77) 1010
34.2%
(Missing) 553
18.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 4
 
0.1%
4 4
 
0.1%
5 9
 
0.3%
6 17
 
0.6%
7 21
 
0.7%
8 39
1.3%
9 66
2.2%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
99 240
8.1%
98 210
7.1%
97 156
5.3%
96 167
5.7%
95 150
5.1%
94 128
4.3%
93 101
3.4%
92 92
 
3.1%
91 76
 
2.6%
89 71
 
2.4%

Корь
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct958
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2411.4861
Minimum0
Maximum212183
Zeros989
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.734689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3362
95-th percentile9968.05
Maximum212183
Range212183
Interquartile range (IQR)362

Descriptive statistics

Standard deviation11437.95
Coefficient of variation (CV)4.7431124
Kurtosis115.45365
Mean2411.4861
Median Absolute Deviation (MAD)17
Skewness9.4647269
Sum7123530
Variance1.3082669 × 108
MonotonicityNot monotonic
2023-04-03T15:24:25.783216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 989
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
6 29
 
1.0%
7 28
 
0.9%
5 25
 
0.8%
8 24
 
0.8%
9 22
 
0.7%
Other values (948) 1588
53.8%
ValueCountFrequency (%)
0 989
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
5 25
 
0.8%
6 29
 
1.0%
7 28
 
0.9%
8 24
 
0.8%
9 22
 
0.7%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
141258 1
< 0.1%
133802 1
< 0.1%
131441 1
< 0.1%
124219 1
< 0.1%
118712 1
< 0.1%
110927 1
< 0.1%
109023 1
< 0.1%

ИМТ
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct608
Distinct (%)20.8%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean38.249007
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.833751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q119.3
median43.15
Q356.1
95-th percentile64.705
Maximum87.3
Range86.3
Interquartile range (IQR)36.8

Descriptive statistics

Standard deviation20.022237
Coefficient of variation (CV)0.52347076
Kurtosis-1.2895832
Mean38.249007
Median Absolute Deviation (MAD)16.35
Skewness-0.2122584
Sum111687.1
Variance400.88995
MonotonicityNot monotonic
2023-04-03T15:24:25.886115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.5 18
 
0.6%
57 16
 
0.5%
55.8 16
 
0.5%
54.2 15
 
0.5%
59.9 15
 
0.5%
59.3 14
 
0.5%
56.5 13
 
0.4%
59.4 13
 
0.4%
55 13
 
0.4%
58.1 13
 
0.4%
Other values (598) 2774
93.9%
(Missing) 34
 
1.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.4 2
 
0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 11
0.4%
2.2 9
0.3%
2.3 6
0.2%
2.4 5
0.2%
2.5 8
0.3%
ValueCountFrequency (%)
87.3 1
< 0.1%
83.3 1
< 0.1%
82.8 1
< 0.1%
81.6 1
< 0.1%
79.3 1
< 0.1%
77.6 1
< 0.1%
77.3 1
< 0.1%
77.1 1
< 0.1%
76.7 1
< 0.1%
76.2 1
< 0.1%

Смертность детей до 5 лет
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.029113
Minimum0
Maximum2500
Zeros785
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:25.935872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q329
95-th percentile137.35
Maximum2500
Range2500
Interquartile range (IQR)29

Descriptive statistics

Standard deviation160.0106
Coefficient of variation (CV)3.8071372
Kurtosis110.36654
Mean42.029113
Median Absolute Deviation (MAD)4
Skewness9.5209274
Sum124154
Variance25603.392
MonotonicityNot monotonic
2023-04-03T15:24:25.986935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 785
26.6%
1 361
 
12.2%
2 163
 
5.5%
4 161
 
5.5%
3 129
 
4.4%
12 53
 
1.8%
8 49
 
1.7%
6 48
 
1.6%
10 47
 
1.6%
5 44
 
1.5%
Other values (242) 1114
37.7%
ValueCountFrequency (%)
0 785
26.6%
1 361
12.2%
2 163
 
5.5%
3 129
 
4.4%
4 161
 
5.5%
5 44
 
1.5%
6 48
 
1.6%
7 30
 
1.0%
8 49
 
1.7%
9 40
 
1.4%
ValueCountFrequency (%)
2500 1
< 0.1%
2400 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2100 1
< 0.1%
2000 2
0.1%
1900 1
< 0.1%
1800 1
< 0.1%
1700 1
< 0.1%
1600 1
< 0.1%

Полио
Real number (ℝ)

Distinct73
Distinct (%)2.5%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.512436
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.038323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.417474
Coefficient of variation (CV)0.28380539
Kurtosis3.7753816
Mean82.512436
Median Absolute Deviation (MAD)6
Skewness-2.0962188
Sum242174
Variance548.37808
MonotonicityNot monotonic
2023-04-03T15:24:26.089851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 376
 
12.7%
98 255
 
8.6%
96 207
 
7.0%
97 205
 
6.9%
95 182
 
6.2%
94 159
 
5.4%
93 121
 
4.1%
92 97
 
3.3%
91 88
 
3.0%
88 71
 
2.4%
Other values (63) 1174
39.7%
ValueCountFrequency (%)
3 7
 
0.2%
4 11
 
0.4%
5 8
 
0.3%
6 11
 
0.4%
7 25
 
0.8%
8 40
1.4%
9 71
2.4%
17 1
 
< 0.1%
23 1
 
< 0.1%
24 2
 
0.1%
ValueCountFrequency (%)
99 376
12.7%
98 255
8.6%
97 205
6.9%
96 207
7.0%
95 182
6.2%
94 159
5.4%
93 121
 
4.1%
92 97
 
3.3%
91 88
 
3.0%
89 57
 
1.9%

Общие расходы
Real number (ℝ)

Distinct818
Distinct (%)30.0%
Missing227
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean5.9394023
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.141068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.93
Q14.27
median5.76
Q37.475
95-th percentile9.76
Maximum17.6
Range17.23
Interquartile range (IQR)3.205

Descriptive statistics

Standard deviation2.4921585
Coefficient of variation (CV)0.41959752
Kurtosis1.1733114
Mean5.9394023
Median Absolute Deviation (MAD)1.58
Skewness0.61840288
Sum16196.75
Variance6.2108538
MonotonicityNot monotonic
2023-04-03T15:24:26.192217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 15
 
0.5%
6.7 12
 
0.4%
5.6 11
 
0.4%
3.4 10
 
0.3%
6.88 10
 
0.3%
5.25 10
 
0.3%
5.64 10
 
0.3%
4.47 10
 
0.3%
7.1 10
 
0.3%
9.1 10
 
0.3%
Other values (808) 2619
88.7%
(Missing) 227
 
7.7%
ValueCountFrequency (%)
0.37 1
 
< 0.1%
0.65 1
 
< 0.1%
0.74 1
 
< 0.1%
0.76 1
 
< 0.1%
0.92 1
 
< 0.1%
1.1 2
0.1%
1.12 3
0.1%
1.15 2
0.1%
1.17 2
0.1%
1.18 3
0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.24 1
< 0.1%
17.2 2
0.1%
17.14 1
< 0.1%
17 1
< 0.1%
16.9 1
< 0.1%
16.61 1
< 0.1%
16.2 1
< 0.1%
15.6 1
< 0.1%
15.57 1
< 0.1%

Дифтерия
Real number (ℝ)

Distinct81
Distinct (%)2.8%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.285179
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.247138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.705305
Coefficient of variation (CV)0.28808718
Kurtosis3.5565918
Mean82.285179
Median Absolute Deviation (MAD)6
Skewness-2.0707088
Sum241507
Variance561.94148
MonotonicityNot monotonic
2023-04-03T15:24:26.298535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 350
 
11.8%
98 254
 
8.6%
97 205
 
6.9%
95 202
 
6.8%
96 201
 
6.8%
94 149
 
5.0%
93 121
 
4.1%
92 100
 
3.4%
91 92
 
3.1%
89 77
 
2.6%
Other values (71) 1184
40.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
 
0.1%
4 12
 
0.4%
5 10
 
0.3%
6 16
 
0.5%
7 22
 
0.7%
8 39
1.3%
9 67
2.3%
16 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
99 350
11.8%
98 254
8.6%
97 205
6.9%
96 201
6.8%
95 202
6.8%
94 149
5.0%
93 121
 
4.1%
92 100
 
3.4%
91 92
 
3.1%
89 77
 
2.6%

ВИЧ/СПИД
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8586662
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.348746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile9.17
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.3882302
Coefficient of variation (CV)2.8989768
Kurtosis31.048587
Mean1.8586662
Median Absolute Deviation (MAD)0
Skewness5.1507013
Sum5490.5
Variance29.033025
MonotonicityNot monotonic
2023-04-03T15:24:26.399975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1781
60.3%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.8 32
 
1.1%
0.9 32
 
1.1%
0.7 29
 
1.0%
1.6 21
 
0.7%
Other values (190) 674
 
22.8%
ValueCountFrequency (%)
0.1 1781
60.3%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.7 29
 
1.0%
0.8 32
 
1.1%
0.9 32
 
1.1%
1 12
 
0.4%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
46.4 1
< 0.1%
43.7 1
< 0.1%
43.5 2
0.1%
42.1 2
0.1%
40.7 1
< 0.1%

ВВП
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2490
Distinct (%)99.4%
Missing448
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean7438.0049
Minimum1.68135
Maximum119172.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.449143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile66.619576
Q1458.98607
median1746.3692
Q35873.3865
95-th percentile41517.057
Maximum119172.74
Range119171.06
Interquartile range (IQR)5414.4004

Descriptive statistics

Standard deviation14235.692
Coefficient of variation (CV)1.9139127
Kurtosis12.424293
Mean7438.0049
Median Absolute Deviation (MAD)1575.8405
Skewness3.2180989
Sum18639640
Variance2.0265492 × 108
MonotonicityNot monotonic
2023-04-03T15:24:26.503452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
325.678573 2
 
0.1%
118.69383 2
 
0.1%
127.47462 2
 
0.1%
111.227396 2
 
0.1%
955.648466 2
 
0.1%
713.63562 2
 
0.1%
65.824121 2
 
0.1%
547.3588785 2
 
0.1%
396.9982166 2
 
0.1%
414.796232 2
 
0.1%
Other values (2480) 2486
84.2%
(Missing) 448
 
15.2%
ValueCountFrequency (%)
1.68135 1
< 0.1%
3.685949 1
< 0.1%
4.6135745 1
< 0.1%
5.6687264 1
< 0.1%
8.376432 1
< 0.1%
11.147277 1
< 0.1%
11.33678 1
< 0.1%
11.553196 1
< 0.1%
11.631377 1
< 0.1%
12.1789279 1
< 0.1%
ValueCountFrequency (%)
119172.7418 1
< 0.1%
115761.577 1
< 0.1%
114293.8433 1
< 0.1%
113751.85 1
< 0.1%
89739.7117 1
< 0.1%
88564.82298 1
< 0.1%
87998.44468 1
< 0.1%
87646.75346 1
< 0.1%
86852.7119 1
< 0.1%
85948.746 1
< 0.1%

Население
Real number (ℝ)

Distinct2278
Distinct (%)99.0%
Missing652
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean12720485
Minimum34
Maximum1.2938593 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.554870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile9844.55
Q1196567.25
median1396804.5
Q37470403.5
95-th percentile47337645
Maximum1.2938593 × 109
Range1.2938593 × 109
Interquartile range (IQR)7273836.2

Descriptive statistics

Standard deviation60803209
Coefficient of variation (CV)4.7799442
Kurtosis300.07523
Mean12720485
Median Absolute Deviation (MAD)1367549
Skewness15.970525
Sum2.9282557 × 1010
Variance3.6970302 × 1015
MonotonicityNot monotonic
2023-04-03T15:24:26.608701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444 4
 
0.1%
12222251 2
 
0.1%
14386649 2
 
0.1%
292 2
 
0.1%
127445 2
 
0.1%
718239 2
 
0.1%
1141 2
 
0.1%
15777451 2
 
0.1%
15411675 2
 
0.1%
155456 2
 
0.1%
Other values (2268) 2280
77.2%
(Missing) 652
 
22.1%
ValueCountFrequency (%)
34 1
< 0.1%
36 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
123 1
< 0.1%
135 1
< 0.1%
146 1
< 0.1%
286 1
< 0.1%
292 2
0.1%
297 1
< 0.1%
ValueCountFrequency (%)
1293859294 1
< 0.1%
1179681239 1
< 0.1%
1161977719 1
< 0.1%
1144118674 1
< 0.1%
1126135777 1
< 0.1%
258162113 1
< 0.1%
255131116 1
< 0.1%
248883232 1
< 0.1%
242524123 1
< 0.1%
236159276 1
< 0.1%

Худоба 1-19 лет
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct200
Distinct (%)6.8%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8516096
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.660088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.4
Q37.2
95-th percentile13.8
Maximum27.7
Range27.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.4150732
Coefficient of variation (CV)0.91002237
Kurtosis3.9491852
Mean4.8516096
Median Absolute Deviation (MAD)2.4
Skewness1.702325
Sum14166.7
Variance19.492872
MonotonicityNot monotonic
2023-04-03T15:24:26.708022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 74
 
2.5%
1.9 65
 
2.2%
0.8 64
 
2.2%
0.7 63
 
2.1%
1.2 63
 
2.1%
2.1 61
 
2.1%
1.5 60
 
2.0%
2.2 58
 
2.0%
2 57
 
1.9%
1.6 57
 
1.9%
Other values (190) 2298
77.8%
ValueCountFrequency (%)
0.1 28
 
0.9%
0.2 40
1.4%
0.3 32
1.1%
0.4 5
 
0.2%
0.5 35
1.2%
0.6 41
1.4%
0.7 63
2.1%
0.8 64
2.2%
0.9 57
1.9%
1 74
2.5%
ValueCountFrequency (%)
27.7 1
 
< 0.1%
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.3 1
 
< 0.1%
27.2 2
0.1%
27.1 2
0.1%
27 3
0.1%
26.9 2
0.1%
26.8 2
0.1%
26.7 1
 
< 0.1%

Худоба 5-9 лет
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct207
Distinct (%)7.1%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8819178
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.759506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.5
median3.4
Q37.2
95-th percentile13.8
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5032798
Coefficient of variation (CV)0.92244073
Kurtosis4.3396012
Mean4.8819178
Median Absolute Deviation (MAD)2.4
Skewness1.7688685
Sum14255.2
Variance20.279529
MonotonicityNot monotonic
2023-04-03T15:24:26.808665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 69
 
2.3%
1.1 67
 
2.3%
1.9 63
 
2.1%
0.5 63
 
2.1%
1 62
 
2.1%
2.1 61
 
2.1%
1.3 60
 
2.0%
1.5 57
 
1.9%
1.7 56
 
1.9%
0.6 54
 
1.8%
Other values (197) 2308
78.1%
ValueCountFrequency (%)
0.1 37
1.3%
0.2 45
1.5%
0.3 25
 
0.8%
0.4 17
 
0.6%
0.5 63
2.1%
0.6 54
1.8%
0.7 46
1.6%
0.8 36
1.2%
0.9 69
2.3%
1 62
2.1%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%
28.1 1
< 0.1%
28 2
0.1%
27.9 1
< 0.1%
27.8 2
0.1%
27.7 1
< 0.1%

Индекс состава доходов
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct625
Distinct (%)22.4%
Missing167
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.62646932
Minimum0
Maximum0.948
Zeros130
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.860933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2783
Q10.491
median0.676
Q30.779
95-th percentile0.892
Maximum0.948
Range0.948
Interquartile range (IQR)0.288

Descriptive statistics

Standard deviation0.21079257
Coefficient of variation (CV)0.33647708
Kurtosis1.3576502
Mean0.62646932
Median Absolute Deviation (MAD)0.129
Skewness-1.1280228
Sum1745.97
Variance0.044433508
MonotonicityNot monotonic
2023-04-03T15:24:26.911760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
4.4%
0.7 17
 
0.6%
0.739 13
 
0.4%
0.636 12
 
0.4%
0.714 12
 
0.4%
0.735 11
 
0.4%
0.86 11
 
0.4%
0.686 11
 
0.4%
0.737 11
 
0.4%
0.734 11
 
0.4%
Other values (615) 2548
86.3%
(Missing) 167
 
5.7%
ValueCountFrequency (%)
0 130
4.4%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.261 1
 
< 0.1%
0.266 1
 
< 0.1%
0.268 3
 
0.1%
0.27 1
 
< 0.1%
0.276 1
 
< 0.1%
0.278 1
 
< 0.1%
0.279 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
 
< 0.1%
0.945 1
 
< 0.1%
0.942 1
 
< 0.1%
0.941 1
 
< 0.1%
0.939 1
 
< 0.1%
0.938 1
 
< 0.1%
0.937 1
 
< 0.1%
0.936 5
0.2%
0.934 2
 
0.1%
0.933 1
 
< 0.1%

Образование
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct173
Distinct (%)6.2%
Missing163
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean11.980365
Minimum0
Maximum20.7
Zeros28
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size23.2 KiB
2023-04-03T15:24:26.963747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.85
Q110.1
median12.3
Q314.2
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation3.3533722
Coefficient of variation (CV)0.27990567
Kurtosis0.88174718
Mean11.980365
Median Absolute Deviation (MAD)2.1
Skewness-0.59249741
Sum33437.2
Variance11.245105
MonotonicityNot monotonic
2023-04-03T15:24:27.013263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9 58
 
2.0%
13.3 52
 
1.8%
12.5 49
 
1.7%
12.8 46
 
1.6%
12.3 44
 
1.5%
12.6 43
 
1.5%
12.4 42
 
1.4%
10.7 41
 
1.4%
11.9 41
 
1.4%
11.7 40
 
1.4%
Other values (163) 2335
79.0%
(Missing) 163
 
5.5%
ValueCountFrequency (%)
0 28
0.9%
2.8 1
 
< 0.1%
2.9 4
 
0.1%
3 1
 
< 0.1%
3.1 1
 
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 2
 
0.1%
ValueCountFrequency (%)
20.7 1
 
< 0.1%
20.6 1
 
< 0.1%
20.5 1
 
< 0.1%
20.4 3
0.1%
20.3 4
0.1%
20.1 2
0.1%
19.8 1
 
< 0.1%
19.7 1
 
< 0.1%
19.5 3
0.1%
19.3 2
0.1%

Interactions

2023-04-03T15:24:23.690989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:05.951956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.992063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.941880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.945121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.854651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.842497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.791585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.770382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.628459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.477768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.359149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.344863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.251938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.109248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.123966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.002214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.893892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.759013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.639510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:23.736460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.037519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.038187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.989696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.993712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.902170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.891512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.837847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.815487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.672390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.523856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.404478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.392603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.297399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.154416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.170588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.048858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.939692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.805079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.684838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:23.778442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.117750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.081104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.035519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.038719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.946586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.939163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.879890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.857258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.716559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.567725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.574826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.437171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.339276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.197376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.213941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.092964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.982254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.849163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.727632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:23.823547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.228259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.192486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.083103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.086421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.993883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.989357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.926789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.902726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.764661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.613938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.619996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.486422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.384576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.242253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.260967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.139535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.027854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.896268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.773453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:23.867782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.278487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.238205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.130779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.133519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.040812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.038876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.975016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.949144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.808725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.660460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.664779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.533991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.429960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.287643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.307463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.187153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.072886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.942929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.819566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:23.912742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.327171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.285999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.179975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.181122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.086662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.088142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.020746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.994041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.852246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.707193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.710181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.580236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.475115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.332920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.353775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.233382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.118799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.989167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.864291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:23.960353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.377107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.334915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.230885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.231050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.137004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.138749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.067272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.041177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.901326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.756257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.757426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.631403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.522977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.380908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.403120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.283504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.167484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.038371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.913365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:24.001975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.421613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.376363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.276656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.276910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.181709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.183560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.109884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.085900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.941604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.798335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.801019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.675795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.565006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.421801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.445379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.326916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.210566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.081623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.955887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:24.041783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.463666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.417938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.319045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.319105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.224299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.228039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.156000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-03T15:24:14.398466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.274076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.264164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.166868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.027766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.042345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.919172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.810009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.677673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.556812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:23.609647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:24.504035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:06.949896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:07.899591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:08.901640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:09.811849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:10.800129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:11.745926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:12.623756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:13.589184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:14.438736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:15.316930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:16.305314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:17.209922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:18.069509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.083943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:19.960793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:20.852696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:21.718467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:22.598858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T15:24:23.649937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-03T15:24:27.067601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ГодПродолжительность жизниСмертность среди взрослыхСмертность младенцевАлкогольПроцентное использование расходовГепатит BКорьИМТСмертность детей до 5 летПолиоОбщие расходыДифтерияВИЧ/СПИДВВПНаселениеХудоба 1-19 летХудоба 5-9 летИндекс состава доходовОбразованиеСтатус
Год1.0000.157-0.051-0.052-0.099-0.0490.102-0.0990.147-0.0520.1110.0790.134-0.0560.1790.045-0.040-0.0390.2010.1940.000
Продолжительность жизни0.1571.000-0.650-0.6040.4380.4300.354-0.2820.586-0.6220.5370.2900.547-0.7570.644-0.093-0.611-0.6210.8660.8140.627
Смертность среди взрослых-0.051-0.6501.0000.395-0.214-0.299-0.2280.144-0.3930.408-0.320-0.171-0.3280.526-0.3850.1000.3900.405-0.549-0.4970.366
Смертность младенцев-0.052-0.6040.3951.000-0.380-0.362-0.3460.571-0.4820.993-0.432-0.216-0.4290.491-0.5100.4500.4560.470-0.580-0.6010.064
Алкоголь-0.0990.438-0.214-0.3801.0000.3010.113-0.1980.323-0.3790.2610.3390.276-0.1930.424-0.009-0.464-0.4580.5100.5480.668
Процентное использование расходов-0.0490.430-0.299-0.3620.3011.0000.106-0.1540.280-0.3640.2130.1630.226-0.2590.807-0.068-0.306-0.3070.5070.4900.448
Гепатит B0.1020.354-0.228-0.3460.1130.1061.000-0.2270.196-0.3460.7930.0440.818-0.3410.260-0.117-0.050-0.0660.3590.3620.173
Корь-0.099-0.2820.1440.571-0.198-0.154-0.2271.000-0.2770.572-0.270-0.187-0.2680.204-0.2140.2930.3100.324-0.230-0.2830.021
ИМТ0.1470.586-0.393-0.4820.3230.2800.196-0.2771.000-0.4940.3260.2660.336-0.5180.483-0.068-0.564-0.5740.6190.6160.459
Смертность детей до 5 лет-0.052-0.6220.4080.993-0.379-0.364-0.3460.572-0.4941.000-0.436-0.221-0.4320.516-0.5170.4440.4640.477-0.591-0.6120.060
Полио0.1110.537-0.320-0.4320.2610.2130.793-0.2700.326-0.4361.0000.1420.921-0.4870.397-0.100-0.223-0.2330.5300.5270.305
Общие расходы0.0790.290-0.171-0.2160.3390.1630.044-0.1870.266-0.2210.1421.0000.158-0.1390.155-0.092-0.358-0.3740.2170.2880.432
Дифтерия0.1340.547-0.328-0.4290.2760.2260.818-0.2680.336-0.4320.9210.1581.000-0.4730.405-0.090-0.236-0.2450.5340.5320.313
ВИЧ/СПИД-0.056-0.7570.5260.491-0.193-0.259-0.3410.204-0.5180.516-0.487-0.139-0.4731.000-0.4830.0970.4780.465-0.652-0.6200.130
ВВП0.1790.644-0.385-0.5100.4240.8070.260-0.2140.483-0.5170.3970.1550.405-0.4831.000-0.052-0.420-0.4290.6960.6660.479
Население0.045-0.0930.1000.450-0.009-0.068-0.1170.293-0.0680.444-0.100-0.092-0.0900.097-0.0521.0000.0800.092-0.058-0.0730.053
Худоба 1-19 лет-0.040-0.6110.3900.456-0.464-0.306-0.0500.310-0.5640.464-0.223-0.358-0.2360.478-0.4200.0801.0000.947-0.578-0.5770.463
Худоба 5-9 лет-0.039-0.6210.4050.470-0.458-0.307-0.0660.324-0.5740.477-0.233-0.374-0.2450.465-0.4290.0920.9471.000-0.577-0.5780.466
Индекс состава доходов0.2010.866-0.549-0.5800.5100.5070.359-0.2300.619-0.5910.5300.2170.534-0.6520.696-0.058-0.578-0.5771.0000.9010.707
Образование0.1940.814-0.497-0.6010.5480.4900.362-0.2830.616-0.6120.5270.2880.532-0.6200.666-0.073-0.577-0.5780.9011.0000.644
Статус0.0000.6270.3660.0640.6680.4480.1730.0210.4590.0600.3050.4320.3130.1300.4790.0530.4630.4660.7070.6441.000

Missing values

2023-04-03T15:24:24.575749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-03T15:24:24.704043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-03T15:24:24.830356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ГодСтатусПродолжительность жизниСмертность среди взрослыхСмертность младенцевАлкогольПроцентное использование расходовГепатит BКорьИМТСмертность детей до 5 летПолиоОбщие расходыДифтерияВИЧ/СПИДВВПНаселениеХудоба 1-19 летХудоба 5-9 летИндекс состава доходовОбразование
02015Развивающаяся65.00263.00620.0171.2865.00115419.10836.008.1665.000.10584.2633736494.0017.2017.300.4810.10
12014Развивающаяся59.90271.00640.0173.5262.0049218.608658.008.1862.000.10612.70327582.0017.5017.500.4810.00
22013Развивающаяся59.90268.00660.0173.2264.0043018.108962.008.1364.000.10631.7431731688.0017.7017.700.479.90
32012Развивающаяся59.50272.00690.0178.1867.00278717.609367.008.5267.000.10669.963696958.0017.9018.000.469.80
42011Развивающаяся59.20275.00710.017.1068.00301317.209768.007.8768.000.1063.542978599.0018.2018.200.459.50
52010Развивающаяся58.80279.00740.0179.6866.00198916.7010266.009.2066.000.10553.332883167.0018.4018.400.459.20
62009Развивающаяся58.60281.00770.0156.7663.00286116.2010663.009.4263.000.10445.89284331.0018.6018.700.438.90
72008Развивающаяся58.10287.00800.0325.8764.00159915.7011064.008.3364.000.10373.362729431.0018.8018.900.438.70
82007Развивающаяся57.50295.00820.0210.9163.00114115.2011363.006.7363.000.10369.8426616792.0019.0019.100.418.40
92006Развивающаяся57.30295.00840.0317.1764.00199014.7011658.007.4358.000.10272.562589345.0019.2019.300.418.10
ГодСтатусПродолжительность жизниСмертность среди взрослыхСмертность младенцевАлкогольПроцентное использование расходовГепатит BКорьИМТСмертность детей до 5 летПолиоОбщие расходыДифтерияВИЧ/СПИДВВПНаселениеХудоба 1-19 летХудоба 5-9 летИндекс состава доходовОбразование
29442009Развивающаяся50.00587.00304.641.0473.0085329.004569.006.2673.0018.1065.821381599.007.507.400.429.90
29452008Развивающаяся48.20632.00303.5620.8475.00028.604675.004.9675.0020.50325.6813558469.007.807.800.429.70
29462007Развивающаяся46.6067.00293.8829.8172.0024228.204673.004.4773.0023.70397.001332999.008.208.200.419.60
29472006Развивающаяся45.407.00284.5734.2668.0021227.904571.005.127.0026.80414.8013124267.008.608.600.419.50
29482005Развивающаяся44.60717.00284.148.7265.0042027.504369.006.4468.0030.30444.77129432.009.009.000.419.30
29492004Развивающаяся44.30723.00274.360.0068.003127.104267.007.1365.0033.60454.3712777511.009.409.400.419.20
29502003Развивающаяся44.50715.00264.060.007.0099826.70417.006.5268.0036.70453.3512633897.009.809.900.429.50
29512002Развивающаяся44.8073.00254.430.0073.0030426.304073.006.5371.0039.8057.35125525.001.201.300.4310.00
29522001Развивающаяся45.30686.00251.720.0076.0052925.903976.006.1675.0042.10548.5912366165.001.601.700.439.80
29532000Развивающаяся46.00665.00241.680.0079.00148325.503978.007.1078.0043.50547.3612222251.0011.0011.200.439.80

Duplicate rows

Most frequently occurring

ГодСтатусПродолжительность жизниСмертность среди взрослыхСмертность младенцевАлкогольПроцентное использование расходовГепатит BКорьИМТСмертность детей до 5 летПолиоОбщие расходыДифтерияВИЧ/СПИДВВПНаселениеХудоба 1-19 летХудоба 5-9 летИндекс состава доходовОбразование# duplicates
02000Развивающаяся46.00665.00241.680.0079.00148325.503978.007.1078.0043.50547.3612222251.0011.0011.200.439.802
12001Развивающаяся45.30686.00251.720.0076.0052925.903976.006.1675.0042.10548.5912366165.001.601.700.439.802
22002Развивающаяся44.8073.00254.430.0073.0030426.304073.006.5371.0039.8057.35125525.001.201.300.4310.002
32003Развивающаяся44.50715.00264.060.007.0099826.70417.006.5268.0036.70453.3512633897.009.809.900.429.502
42004Развивающаяся44.30723.00274.360.0068.003127.104267.007.1365.0033.60454.3712777511.009.409.400.419.202
52005Развивающаяся44.60717.00284.148.7265.0042027.504369.006.4468.0030.30444.77129432.009.009.000.419.302
62006Развивающаяся45.407.00284.5734.2668.0021227.904571.005.127.0026.80414.8013124267.008.608.600.419.502
72007Развивающаяся46.6067.00293.8829.8172.0024228.204673.004.4773.0023.70397.001332999.008.208.200.419.602
82008Развивающаяся48.20632.00303.5620.8475.00028.604675.004.9675.0020.50325.6813558469.007.807.800.429.702
92009Развивающаяся50.00587.00304.641.0473.0085329.004569.006.2673.0018.1065.821381599.007.507.400.429.902